Top Tech Jobs & Startup Jobs

Design and build production-grade AI agents and multi-agent workflows with tool use, API integrations, memory, RAG, evaluation, observability, and failure recovery. Develop scalable Python or Node.js/TypeScript backend services and distributed systems, deploy them on AWS, and own CI/CD, infrastructure, security, reliability, and cost monitoring. Evaluate emerging agent technologies and frameworks.

Lead end-to-end testing and evaluation for AI agent systems, including chatbots, RAG applications, and MCP tool-calling agents. Build evaluation frameworks, golden datasets, regression suites, observability integrations, and automated CI testing. Assess accuracy, groundedness, safety, latency, retrieval quality, and tool execution. Conduct red-teaming, communicate quality risks to client stakeholders, and mentor junior QA resources.

Monitor and resolve data pipeline support issues within SLAs, applying standard procedures or escalating to appropriate teams. Maintain documentation, provide status updates, troubleshoot root causes, and improve support processes. The role requires Python and SQL experience, familiarity with AWS services such as S3 and Redshift, strong communication, and sound judgment when escalating issues.

Analyze and enhance commercial investment data by identifying dataset gaps, sourcing and validating information, writing SQL queries, cleaning large datasets, and building dashboards and reports. The role translates business requirements into analytical tasks, uses AI agents and tools to automate data workflows, supports ad hoc analyses, documents data processes, and presents actionable findings to nontechnical stakeholders.

Design, develop, test, deploy, and maintain full-stack client applications across databases, APIs, frontends, and cloud infrastructure. Build Node.js and Python backend services, ReactJS interfaces, and MySQL/PostgreSQL databases. Use LLM-assisted development tools, manage applications post-launch, contribute to architecture decisions, collaborate directly with clients, and support multiple engagements through debugging, enhancements, performance tuning, documentation, and code reviews.

Design, maintain, and improve web scrapers for alternative datasets; clean, transform, validate, and load data into databases or warehouses; orchestrate pipelines with Airflow; and build quality checks, alerts, and data tools. Collaborate with analysts and cross-functional teams to define data requirements, investigate time-sensitive incidents, and support investment decision-making through reliable datasets and notifications.